Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine

Song, Yuedong and Zhang, Jiaxiang ORCID: https://orcid.org/0000-0002-4758-0394 2016. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. Journal of Neuroscience Methods 257 , pp. 45-54. 10.1016/j.jneumeth.2015.08.026

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Abstract

Background Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings. New method Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach. Results The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Publisher:	Elsevier
ISSN:	0165-0270
Date of Acceptance:	20 August 2015
Last Modified:	31 Oct 2022 09:01
URI:	https://orca.cardiff.ac.uk/id/eprint/79560

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